Triple
T12108608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Inquirer |
E288364
|
entity |
| Predicate | fictionalProductType |
P81118
|
FINISHED |
| Object | daily newspaper |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: daily newspaper | Statement: [New York Inquirer, fictionalProductType, daily newspaper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalProductType Context triple: [New York Inquirer, fictionalProductType, daily newspaper]
-
A.
fictionalObject
Indicates that one entity is a fictional or imaginary object in relation to another entity.
-
B.
fictionalEntityType
chosen
Indicates that the subject is classified as a particular type or category of fictional entity within a narrative or imaginary context.
-
C.
fictionalMaterial
Indicates that something is made of, composed of, or incorporates a material that exists only in fiction or imagination.
-
D.
hasFictionalProductionType
Indicates that an entity is associated with a specific type or category of fictional production (such as a genre, format, or style).
-
E.
featuresFictionalTechnology
Indicates that an entity includes, depicts, or makes use of imagined or speculative technology that does not exist in reality.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.